WO1999014741A1 - Method for recognising a keyword in speech - Google Patents
Method for recognising a keyword in speech Download PDFInfo
- Publication number
- WO1999014741A1 WO1999014741A1 PCT/DE1998/002633 DE9802633W WO9914741A1 WO 1999014741 A1 WO1999014741 A1 WO 1999014741A1 DE 9802633 W DE9802633 W DE 9802633W WO 9914741 A1 WO9914741 A1 WO 9914741A1
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- WIPO (PCT)
- Prior art keywords
- confidence measure
- keyword
- sequence
- key word
- states
- Prior art date
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 238000005070 sampling Methods 0.000 claims abstract description 17
- 239000013598 vector Substances 0.000 claims description 23
- 230000008569 process Effects 0.000 description 5
- 230000007704 transition Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 101000822695 Clostridium perfringens (strain 13 / Type A) Small, acid-soluble spore protein C1 Proteins 0.000 description 1
- 101000655262 Clostridium perfringens (strain 13 / Type A) Small, acid-soluble spore protein C2 Proteins 0.000 description 1
- 101000655256 Paraclostridium bifermentans Small, acid-soluble spore protein alpha Proteins 0.000 description 1
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- 230000006978 adaptation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/14—Speech classification or search using statistical models, e.g. Hidden Markov Models [HMMs]
- G10L15/142—Hidden Markov Models [HMMs]
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L2015/088—Word spotting
Definitions
- the invention relates to a method for recognizing a keyword in spoken language.
- a method for recognizing a keyword is known from [1], which uses a speech recognizer with a large vocabulary. An attempt is made to fully recognize the spoken language. The recognized words are then examined for any existing keywords. This method is complex and error-prone because of the large vocabulary and because of the problems with modeling spontaneous expressions and noises, that is to say part of the speech signal that cannot be clearly assigned to a word.
- special fill models also: filleting, garbage models
- OOV parts, OOV Out of Vocabulary
- Models This method provides a sequence of fill and keyword symbols. It is difficult to design or train a suitable filler model that stands out well from the modeled key words, that is, it is highly discriminatory with regard to the key word models.
- Hidden Markov models are also known from [3] or [4]. It is also known from [3] or [4] to determine a best path using the Viterbi algorithm.
- HMMs Hidden Markov models
- discrete stochastic processes also called Markov processes.
- hidden Markov models are used, among other things. to build a word lexicon in which the word models constructed from subunits are listed.
- a hidden Markov model is formally described by:
- emission matrix B which includes emission probabilities B ⁇ :
- the Markov chain s . a range of values
- this range of values containing a finite set of N states is called q t .
- the emission probability B- j _ k results from the occurrence of a certain symbol ⁇ ⁇ in state S j _ zu
- a state space from hidden Markov models results in that each state of the Hidden Markov model can have a predefined set of successor states: itself, the next state, the state after next, etc.
- the state space with all possible transitions is called a trellis .
- hidden Markov models of order 1 For hidden Markov models of order 1, a past more than one time step is irrelevant.
- the Viterbi algorithm is based on the idea that if you are locally on an optimal path in the state space (trellis), it is always part of a global optimal path. Because of order 1 of the Hidden Markov Models can only be considered the best predecessor of a condition, since the worse predecessors received a poorer rating in advance. This means that you can search recursively, starting from the first point in time, step by time, the optimal path by determining all possible continuations of the path for each time step and selecting only the best continuation.
- the object of the invention is to provide a method which enables the recognition of a keyword in spoken language, the disadvantages described above being avoided.
- a method for recognizing a key word in spoken language is specified, the key word being represented by a sequence of states W.
- the spoken language is scanned at a predetermined rate and at each sampling time t a feature vector 0 t for one belonging to the sampling time t Speech signal created from the spoken language.
- a sequence 0 of feature vectors O t is mapped to the sequence of states W by means of a Viterbi algorithm, in which case a local confidence measure is an emission measure, preferably the negative logarithm an emission probability.
- the Viterbi algorithm provides a global confidence measure C (also: confidence measure C).
- the key word is recognized in the spoken language if:
- T a predetermined threshold bbeezzeeiicchhnneenn.
- An advantage of the invention is that a key word is recognized within the spoken language without the utterance having to be modeled as a whole. This results in a significantly reduced effort in the implementation and consequently also a more efficient (faster) procedure.
- the (global) confidence measure C as a basic decoding principle, acoustic modeling within the decoding process is limited to the key words.
- a further development consists in that a new path through the state space of the hidden Markov models begins in a first state of the sequence of states W at each sampling time t.
- a new path through the state space of the hidden Markov models begins in a first state of the sequence of states W at each sampling time t.
- the confidence measure On the basis of the confidence measure, feature vectors resulting from subsequent sampling times are mapped to the states of the keyword represented by hidden Markov models.
- there is a global confidence measure on the basis of which a decision is made as to whether the supposed beginning of the keyword was really such. If so, the keyword is recognized, otherwise it is not recognized.
- the global confidence measure C is determined by
- s ⁇ ) denote the emission probability
- P (sj) the probability for the state sj
- P (O t ) the probability for the feature vector O t .
- a suitable global confidence measure is characterized by the property that it provides information about the degree of reliability with which a keyword is detected. In the negative logarithmic range, a small press
- the confidence measure C is determined by P (0
- the definitions of the local confidence measures can be derived from the definitions of the global confidence measures. Local confidence measures at those points in time that coincide with the utterance of this keyword are included in the calculation of the confidence measure for a keyword.
- the local confidence measures can be calculated.
- An example of such an approximation method is the averaging of the n best emissions -log Pl ⁇ t Sj] at every point in time t.
- the decoding process is usually carried out using the Viterbi algorithm:
- Descriptive form similar, it is useful to use a mechanism that, when a key word is recognized, rules out that another key word was partially contained in the speech signal spoken during the period of the recognized key word.
- 1 shows a block diagram of a method for recognizing a keyword in spoken language
- 2 shows a sketch which illustrates the determination of a confidence measure
- FIG. 3 shows a sketch like FIG. 3, which shows the course of an assumed confidence measure over a predetermined period of time.
- FIG. 1 shows a block diagram of a method for recognizing a keyword in continuous language.
- the keyword is represented by a sequence of states W.
- phoneme HMMs with three states each are used (see [3]).
- a next step 102 the continuous speech is sampled and at each sampling time t a feature vector 0 t for a speech signal belonging to the sampling time t is created from the continuous speech.
- the feature vector 0 ⁇ comprises a predetermined set of features which are characteristic of the speech signal at the sampling time t, as components.
- a sequence of feature vectors which have been obtained from the speech signal for different sampling times t are mapped onto the sequence of states W.
- the Viterbi algorithm represents a mapping rule (see [3]).
- sj) used in the Viterbi algorithm is replaced by a local confidence measure.
- the Viterbi algorithm delivers a global confidence measure C, that is, for those found
- States of the sequence of states W include individual local confidence measures cumulatively.
- the key word is recognized in a step 105 in the continuous language if applies
- W is the keyword, represented as one
- T designate a predetermined threshold
- the keyword is not recognized in the continuous language.
- the first global confidence measure is defined from the negative logarithm of an a posteriori probability for the keyword as a measure of reliability:
- iw ⁇ P Ot ⁇ (t) J • a ⁇ (tl), ⁇ (t) (10) t
- the probability for a sequence of feature vectors P (0) is expressed as a multiplication of probabilities for individual feature vectors P (0 t ).
- the probability for an entire word P (W) is calculated by multiplying the individual probabilities u / CtH of each individual selected state of an HMM ⁇ , the function ⁇ (t) mapping the characteristic vectors (al ⁇ o of time) is the state of the keyword.
- w) corresponds to the usual probability of the HMM ⁇ , which can be calculated using the emission probabilities p [0 t ⁇ ⁇ ⁇ - ⁇ 1 and the transition probabilities a-vi / f -l) v j / (t) .
- the global confidence measure C ⁇ results in:
- the probability of the feature vector appearing in the denominator of equation (12) can be calculated by taking into account all states of the HMM:
- a second global confidence measure consists of the ratio of the conditional probabilities of a sequence 0 of feature vectors O j - once under the condition of a sequence of states W characterizing that keyword and another time under the inverse model W. It results in:
- W represents only a model that does not actually exist, but whose emission probability can be calculated.
- this definition leads to a symmetrical global confidence measure that has a symmetry center at 0 if
- the local confidence measure c 2 0 t ⁇ j can be calculated, since the denominator can be calculated by calculating all weighted emission probabilities except for PIO t s itself:
- both definitions lead to a confidence measure that in the case of a low value (in the case of the global confidence measure C 2 a negative value) indicates a high degree of reliability that a keyword has been correctly recognized.
- Confidence measures can be calculated using general phoneme HMMs.
- the continuous speech signal can contain several, also different, key words, preferably only one key word being contained at a time.
- the continuous speech signal is sampled at discrete points in time and the information available at the respective sampling point in time is stored in a feature vector O t .
- a keyword can begin at each of these sampling times. So at each of the times t1, t2 or t3 a potential keyword begins, the paths of which can recombine in the course of the Viterbi algorithm.
- a keyword is assumed here, with several keywords each requiring a method for each keyword to be recognized.
- the feature vectors O t obtained from the continuous language are used to map the feature vectors following the time ⁇ .
- the best path PF is determined with respect to the accumulated confidence measure. This results in a confidence measure C for each time t.
- the value of the confidence measure provides information as to whether the keyword was contained in the continuous language or not and ended at time t.
- a global confidence measure C is observed at every point in time t, an associated starting point in time being determined by using the Viterbi algorithm.
- the measure of confidence is correspondingly bad; there is no recognition. Also, according to the mode of operation of the Viterbi algorithm, the length for different paths for determining the global confidence measure is not the same, indicated by the fact that the global confidence measure C is made up of the local confidence measures of four
- Figure 3 illustrates this relationship.
- the global confidence measures C 1 , C 11 and C determined from FIG. 2 are plotted on the ordinate in FIG. 3.
- the abscissa again indicates the time t.
- a minimum MIN of the global confidence measure C is preferably determined and it is therefore assumed that the keyword MIN is present in the continuous language in this minimum MIN. This is important insofar as the threshold T for the global confidence measure C is already undershot at a time t a , so that the keyword is recognized.
- variable dynamic adaptation different time periods for determining the global global confidence measure C
- this keyword can be recognized "even better" at impending times t a + j _.
- the minimum MIN is determined with the associated time t MIN . From this point in time t j v j N
- Backtracking determines the start time of the key word in the continuous speech signal. The beginning of the spoken key word is thus determined in the continuous speech signal.
- the key word is preferably the correct one, the degree of confidence of which, in comparison with the other key words, reflects the highest level of reliability.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
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Abstract
Description
Claims
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP98954132A EP1012828B1 (en) | 1997-09-18 | 1998-09-07 | Method for recognising a keyword in speech |
DE59801227T DE59801227D1 (en) | 1997-09-18 | 1998-09-07 | METHOD FOR RECOGNIZING A KEYWORD IN SPOKEN LANGUAGE |
US09/486,428 US6505156B1 (en) | 1997-09-18 | 1998-09-07 | Method for recognizing a keyword in speech |
JP2000512198A JP2001516904A (en) | 1997-09-18 | 1998-09-07 | How to recognize keywords in spoken language |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19741211 | 1997-09-18 | ||
DE19741211.4 | 1997-09-18 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1999014741A1 true WO1999014741A1 (en) | 1999-03-25 |
Family
ID=7842841
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/DE1998/002633 WO1999014741A1 (en) | 1997-09-18 | 1998-09-07 | Method for recognising a keyword in speech |
Country Status (7)
Country | Link |
---|---|
US (1) | US6505156B1 (en) |
EP (1) | EP1012828B1 (en) |
JP (1) | JP2001516904A (en) |
CN (1) | CN1125433C (en) |
DE (1) | DE59801227D1 (en) |
ES (1) | ES2161550T3 (en) |
WO (1) | WO1999014741A1 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001048738A1 (en) * | 1999-12-23 | 2001-07-05 | Intel Corporation | A global approach for segmenting characters into words |
CN108010515A (en) * | 2017-11-21 | 2018-05-08 | 清华大学 | A kind of speech terminals detection and awakening method and device |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1215654B1 (en) * | 2000-12-13 | 2006-05-24 | Sony Deutschland GmbH | Method for recognizing speech |
US6985859B2 (en) * | 2001-03-28 | 2006-01-10 | Matsushita Electric Industrial Co., Ltd. | Robust word-spotting system using an intelligibility criterion for reliable keyword detection under adverse and unknown noisy environments |
US7356466B2 (en) * | 2002-06-28 | 2008-04-08 | Samsung Electronics Co., Ltd. | Method and apparatus for performing observation probability calculations |
US20040006470A1 (en) * | 2002-07-03 | 2004-01-08 | Pioneer Corporation | Word-spotting apparatus, word-spotting method, and word-spotting program |
US7974411B2 (en) * | 2008-01-31 | 2011-07-05 | International Business Machines Corporation | Method for protecting audio content |
US7978853B2 (en) * | 2008-01-31 | 2011-07-12 | International Business Machines Corporation | System and computer program product for protecting audio content |
US9293130B2 (en) * | 2008-05-02 | 2016-03-22 | Nuance Communications, Inc. | Method and system for robust pattern matching in continuous speech for spotting a keyword of interest using orthogonal matching pursuit |
CN105261356A (en) * | 2015-10-30 | 2016-01-20 | 桂林信通科技有限公司 | Voice recognition system and method |
US10311874B2 (en) | 2017-09-01 | 2019-06-04 | 4Q Catalyst, LLC | Methods and systems for voice-based programming of a voice-controlled device |
CN108959262B (en) * | 2018-07-09 | 2022-07-26 | 鼎富智能科技有限公司 | Named entity identification method and device |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
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US5440662A (en) * | 1992-12-11 | 1995-08-08 | At&T Corp. | Keyword/non-keyword classification in isolated word speech recognition |
US5832430A (en) * | 1994-12-29 | 1998-11-03 | Lucent Technologies, Inc. | Devices and methods for speech recognition of vocabulary words with simultaneous detection and verification |
US5842163A (en) * | 1995-06-21 | 1998-11-24 | Sri International | Method and apparatus for computing likelihood and hypothesizing keyword appearance in speech |
US5797123A (en) * | 1996-10-01 | 1998-08-18 | Lucent Technologies Inc. | Method of key-phase detection and verification for flexible speech understanding |
US6125345A (en) * | 1997-09-19 | 2000-09-26 | At&T Corporation | Method and apparatus for discriminative utterance verification using multiple confidence measures |
US6571210B2 (en) * | 1998-11-13 | 2003-05-27 | Microsoft Corporation | Confidence measure system using a near-miss pattern |
-
1998
- 1998-09-07 ES ES98954132T patent/ES2161550T3/en not_active Expired - Lifetime
- 1998-09-07 EP EP98954132A patent/EP1012828B1/en not_active Expired - Lifetime
- 1998-09-07 CN CN98809296A patent/CN1125433C/en not_active Expired - Fee Related
- 1998-09-07 DE DE59801227T patent/DE59801227D1/en not_active Expired - Lifetime
- 1998-09-07 JP JP2000512198A patent/JP2001516904A/en not_active Withdrawn
- 1998-09-07 WO PCT/DE1998/002633 patent/WO1999014741A1/en active IP Right Grant
- 1998-09-07 US US09/486,428 patent/US6505156B1/en not_active Expired - Lifetime
Non-Patent Citations (3)
Title |
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COX S ET AL: "Confidence measures for the SWITCHBOARD database", 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING CONFERENCE PROCEEDINGS (CAT. NO.96CH35903), 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING CONFERENCE PROCEEDINGS, ATLANTA, GA, USA, 7-10 M, ISBN 0-7803-3192-3, 1996, New York, NY, USA, IEEE, USA, pages 511 - 514 vol. 1, XP002092086 * |
LLEIDA E ET AL: "Likelihood ratio decoding and confidence measures for continuous speech recognition", PROCEEDINGS ICSLP 96. FOURTH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING (CAT. NO.96TH8206), PROCEEDING OF FOURTH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING. ICSLP '96, PHILADELPHIA, PA, USA, 3-6 OCT. 1996, ISBN 0-7803-3555-4, 1996, New York, NY, USA, IEEE, USA, pages 478 - 481 vol.1, XP002092087 * |
RIVLIN Z ET AL: "A phone-dependent confidence measure for utterance rejection", 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING CONFERENCE PROCEEDINGS (CAT. NO.96CH35903), 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING CONFERENCE PROCEEDINGS, ATLANTA, GA, USA, 7-10 M, ISBN 0-7803-3192-3, 1996, New York, NY, USA, IEEE, USA, pages 515 - 517 vol. 1, XP002092085 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001048738A1 (en) * | 1999-12-23 | 2001-07-05 | Intel Corporation | A global approach for segmenting characters into words |
CN108010515A (en) * | 2017-11-21 | 2018-05-08 | 清华大学 | A kind of speech terminals detection and awakening method and device |
CN108010515B (en) * | 2017-11-21 | 2020-06-30 | 清华大学 | Voice endpoint detection and awakening method and device |
Also Published As
Publication number | Publication date |
---|---|
CN1125433C (en) | 2003-10-22 |
CN1270688A (en) | 2000-10-18 |
JP2001516904A (en) | 2001-10-02 |
DE59801227D1 (en) | 2001-09-20 |
EP1012828B1 (en) | 2001-08-16 |
EP1012828A1 (en) | 2000-06-28 |
ES2161550T3 (en) | 2001-12-01 |
US6505156B1 (en) | 2003-01-07 |
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